Orthogonal Time Frequency Space and Predictive Beamforming-Enabled URLLC in Vehicular Networks

Yuan, W; Zou, J; Cui, Y; Li, X; Mu, J; Han, K

Orthogonal Time Frequency Space and Predictive Beamforming-Enabled URLLC in Vehicular Networks

Yuan, W Zou, J Cui, Y Li, X Mu, J Han, K

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Wireless Communications, 2023, 30, (2), pp. 56-62
Issue Date:: 2023-04-01

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Field	Value	Language
dc.contributor.author	Yuan, W
dc.contributor.author	Zou, J
dc.contributor.author	Cui, Y
dc.contributor.author	Li, X
dc.contributor.author	Mu, J
dc.contributor.author	Han, K
dc.date.accessioned	2024-05-13T04:18:28Z
dc.date.available	2024-05-13T04:18:28Z
dc.date.issued	2023-04-01
dc.identifier.citation	IEEE Wireless Communications, 2023, 30, (2), pp. 56-62
dc.identifier.issn	1536-1284
dc.identifier.issn	1558-0687
dc.identifier.uri	http://hdl.handle.net/10453/178893
dc.description.abstract	To fulfill the requirements of future intelligent transportation system in 6G era, ultra-reliable and low-latency vehicular communications is of great importance. In high-mobility scenarios, the conventional orthogonal frequency division multiplexing (OFDM) modulation may fail to work due to high Doppler spreads. Moreover, the dynamic network topology imposes challenges on aligning the beams in multiple antenna systems. In this context, this article introduces a new orthogonal time frequency space (OTFS) and sensing-assisted predictive beamforming framework for supporting reliable and low-latency vehicular communications. In particular, we will first overview the OTFS modulation scheme, which performs data transmission in the delay-Doppler (DD) domain and discuss its superiority in improving communication reliability in vehicular networks. Then the sensing-assisted predictive beamforming scheme will be introduced, which does not rely on dedicated pilots for beam pairing, leading to very low overhead and latency. Benefiting from the DD channel representation, we briefly discuss the potential of channel prediction. Lastly, the challenges and future research directions are summarized.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Wireless Communications
dc.relation.isbasedon	10.1109/MWC.005.2200408
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Orthogonal Time Frequency Space and Predictive Beamforming-Enabled URLLC in Vehicular Networks
dc.type	Journal Article
utslib.citation.volume	30
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-13T04:18:26Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	2

Abstract:

To fulfill the requirements of future intelligent transportation system in 6G era, ultra-reliable and low-latency vehicular communications is of great importance. In high-mobility scenarios, the conventional orthogonal frequency division multiplexing (OFDM) modulation may fail to work due to high Doppler spreads. Moreover, the dynamic network topology imposes challenges on aligning the beams in multiple antenna systems. In this context, this article introduces a new orthogonal time frequency space (OTFS) and sensing-assisted predictive beamforming framework for supporting reliable and low-latency vehicular communications. In particular, we will first overview the OTFS modulation scheme, which performs data transmission in the delay-Doppler (DD) domain and discuss its superiority in improving communication reliability in vehicular networks. Then the sensing-assisted predictive beamforming scheme will be introduced, which does not rely on dedicated pilots for beam pairing, leading to very low overhead and latency. Benefiting from the DD channel representation, we briefly discuss the potential of channel prediction. Lastly, the challenges and future research directions are summarized.

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http://hdl.handle.net/10453/178893